• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.42-47
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• 2005

In this papcr, we address the 3D reconstruction of the indoor circumstance using what the data is extracted by a pall of image from Stereo Camera. Generally sucaking, there arc three methods to extract 3-Dimensional data using IR sensor, Laser sensor and Stereo camera sensor. The best is stereo camera sensor which can show a high performance at a reasonable price. We used 'Window Correlation Matching Method' to extract 3-Dimensional data in stereo image. We proposed new Method to reduce error data, said 'Histogram Weighted Hough Transform'. Owing to this mettled, we reduced error data in each stereo image. So reconstruction is well done. 3-Dimensional Reconstruction is accomplished by using the DirectX that is well known as 3D-Game development tool. We show that the stereo camera can be not only used to extract 3-dimensional data but also applied to reconstruct the 3-Dimensional circumstance. And we try to reduce the error data using various method.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.412-417
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• 2015

Kinect sensor has two output data which are produced from red green blue (RGB) sensor and depth sensor, it is called color image and depth map, respectively. Although this device's prices are cheapest than the other devices for three-dimensional (3D) reconstruction, we need extra work for reconstruct a smooth 3D data and also have semantic meaning. It happened because the depth map, which has been produced from depth sensor usually have a coarse and empty value. Consequently, it can be make artifact and holes on the surface, when we reconstruct it to 3D directly. In this paper, we present a method for solving this problem by using implicit surface representation. The key idea for represent implicit surface is by using radial basis function (RBF) and to avoid the trivial solution that the implicit function is zero everywhere, we need to defined on-surface point and off-surface point. Based on our simulation results using captured face as an input, we can produce smooth 3D face and fill the holes on the 3D face surface, since RBF is good for interpolation and holes filling. Modified anisotropic diffusion is used to produced smoothed surface.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.107-109
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• 2004

In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile information. The proposed system focuses on improving recognition performance of 3D object. Unlike the conventional object recognition system that uses image sensor alone, the proposed method uses tactual sensors in addition to visual sensor. Neural network is used to fuse these informations. Tactual signals are obtained from the reaction force by the pressure sensors at the fingertips when unknown objects are grasped by four-fingered robot hand. The experiment evaluates the recognition rate and the number of teaming iterations of various objects. The merits of the proposed systems are not only the high performance of the learning ability but also the reliability of the system with tactual information for recognizing various objects even though visual information has a defect. The experimental results show that the proposed system can improve recognition rate and reduce learning time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme of 3D object.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.6
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• pp.26-34
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• 1999

This study shows an alternative method for the determination of object's position, based on a computer vision method. This approach develops the vision system model to define the reciprocal relationship between the 3-D real space and 2-D image plane. The developed model involves the bilinear six-view parameters, which is estimated using the relationship between the camera space location and real coordinates of known position. Based on estimated parameters in independent cameras, the position of unknown object is accomplished using a sequential estimation scheme that permits data of unknown points in each of the 2-D image plane of cameras. This vision control methods the robust and reliable, which overcomes the difficulties of the conventional research such as precise calibration of the vision sensor, exact kinematic modeling of the robot, and correct knowledge of the relative positions and orientation of the robot and CCD camera. Finally, the developed vision control method is tested experimentally by performing determination of object position in the space using computer vision system. These results show the presented method is precise and compatible.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1453-1461
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• 2022

The accuracy of peripheral object recognition algorithms using 3D data sensors such as LiDAR in autonomous vehicles has been increasing through many studies, but this requires high performance hardware and complex structures. This object recognition algorithm acts as a large load on the main processor of an autonomous vehicle that requires performing and managing many processors while driving. To reduce this load and simultaneously exploit the advantages of 3D sensor data, we propose 2D data-based recognition using the ROI generated by extracting physical properties from 3D sensor data. In the environment where the brightness value was reduced by 50% in the basic image, it showed 5.3% higher accuracy and 28.57% lower performance time than the existing 2D-based model. Instead of having a 2.46 percent lower accuracy than the 3D-based model in the base image, it has a 6.25 percent reduction in performance time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.2
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• pp.157-164
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• 2011

As high-resolution satellite images have enabled large scale topographic mapping and monitoring on global scale with short revisit time, agile sensor orientation, and large swath width, many countries make effort to secure the satellite image information. In Korea, KOMPSAT-2 (KOrea Multi-Purpose SATellite-2) was launched in July 28 2006 with high specification. These satellites have stereo image acquisition capability for 3D mapping and monitoring. To efficiently handle stereo images such as stereo display and monitoring, the accurate epipolar image generation process is prerequisite. However, the process was highly limited due to complexity in epipolar geometry of pushbroom sensor. Recently, the piecewise approach to generate epipolar images using RPC was developed and tested for in-track IKONOS stereo images. In this paper, the piecewise approach was tested for KOMPSAT-2 across-track stereo images to see how accurately KOMPSAT-2 epipolar images can be generated for 3D geospatial applications. In the experiment, two across-track stereo sets from three KOMPSAT-2 images of different dates were tested using RPC as the sensor model. The test results showed that one-pixel level of y-parallax was achieved for manually measured tie points.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.3
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• pp.291-300
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• 2001

In this paper, we developed a Satellite Image Processing System for obtaining 3-D positional information which is composed of five process modules. As a procedure of them, the Data Process module is the procedure that reads and processes the header file to generate data files. and then calculates orbital parameters and sensor attitudes for obtaining of 3-D positional information with them. The 3D Process module is to calculate 3-D positional information and the Dialog Process module is to correct the time of image frame center using the single image or stereo images for implementing the 3D Process module. We expect to obtain 3-D positional information with the header file and minimum GCPs(1∼2 points) using this system efficiently and economically in comparison with existing commercial software packages.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.480-485
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• 2002

The purpose of this paper is the development of guidance algorithm for a mobile robot which is used to acquire the position and state information of the pipe defects such as crack, damage and through hole. The data used for the algorithm is the range data obtained by the range sensor which is based on an optical triangulation method. The sensor, which consists of a laser slit beam and a CCD camera, measures the 3D profile of the pipe's inner surface. After setting the range sensor on the robot, the robot is put into a pipe. While the camera and the LSB sensor part is rotated about the robot axis, a laser slit beam (LSB) is projected onto the inner surface of the pipe and a CCD camera captures the image. From the images the range data is obtained with respect to the sensor coordinate through a series of image processing and applying the sensor matrix. After the data is transformed into the robot coordinate, the position and orientation of the robot should be obtained in order to guide the robot. In addition, analyzing the data, 3D shape of the pipe is constructed and the numerical data for the defects of the pipe can be found. These data will be used for pipe maintenance and service.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.518-523
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• 2002

Nowadays the Rational Function Model (RFM), an abstract sensor model, is substituting physical sensor models for highly complicated imaging geometry. But RFM is algorithm to be required many Ground Control Points (GCP). In case of RFM of the third order, At least forty GCP are required far RFM generation. The purpose of this study is to research more efficient algorithm on GCP and accurate algorithm similar to RFM. The Polynomial Camera Model is relatively accurate and requires a little GCP in comparisons of RFM. This paper introduces how to generate Polynomial Camera Model and fundamental algorithms for construction of 3-D topographic data using the Polynomial Camera Model information in the Kompsat stereo pair and describes how to generate the 3-D ground coordinates by manual matching. Finally we tried to extract height information for the whole image area with the stereo matching technique based on the correlation.

• Journal of Sensor Science and Technology
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• v.6 no.2
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• pp.123-130
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• 1997

In this paper, we present an original stereo vision system which comprises two process: 1. An image segmentation algorithm based on new concept called declivity and using automatic thresholds. 2. A new stereo matching algorithm based on an optimal path search. This path is obtained by dynamic programming method which uses the threshold values calculated during the segmentation process. At present, a complete depth map of indoor scene only needs about 3 s on a Sun workstation IPX, and this time will be reduced to a few tenth of second on a specialised architecture based on several DSPs which is currently under consideration.